diff --git a/segment_data.py b/segment_data.py
index 9d95f57..51530f5 100644
--- a/segment_data.py
+++ b/segment_data.py
@@ -156,8 +156,8 @@ def create_segments_tfrecords(collection_name='story_test_segments',sample_count
             f_bounds = [spec_frame(phrase_spec,b) for b in ph_bounds]
             valid_bounds = [i for i in f_bounds if 0 < i < spec_n]
             b_frames = np.asarray(valid_bounds)
-            # print(spec_n,b_frames)
-            result[b_frames] = 1
+            if len(b_frames) > 0:
+                result[b_frames] = 1
             nonlocal n_records,n_spec,n_features
             n_spec = max([n_spec,spec_n])
             n_features = spec_w
@@ -178,9 +178,10 @@ def create_segments_tfrecords(collection_name='story_test_segments',sample_count
 
     word_groups = [i for i in audio_samples.groupby('phrase')]
     wg_sampled = reservoir_sample(word_groups,sample_count) if sample_count > 0 else word_groups
+    # write_samples(word_groups,'all')
     tr_audio_samples,te_audio_samples = train_test_split(wg_sampled,test_size=train_test_ratio)
     write_samples(tr_audio_samples,'train')
-    # write_samples(te_audio_samples,'test')
+    write_samples(te_audio_samples,'test')
     const_file = './outputs/segments/'+collection_name+'/constants.pkl'
     pickle.dump((n_spec,n_features,n_records),open(const_file,'wb'))
 
@@ -257,7 +258,7 @@ if __name__ == '__main__':
     # plot_segments('story_test_segments')
     # fix_csv('story_phrases')
     # pass
-    create_segments_tfrecords('story_phrases', sample_count=100)
+    create_segments_tfrecords('story_phrases_full', sample_count=0)
     # record_generator,input_data,output_data,copy_read_consts = read_segments_tfrecords_generator('story_test')
     # tr_gen = record_generator()
     # for i in tr_gen:
diff --git a/segment_model.py b/segment_model.py
index 7fc5be3..ce0c6cf 100644
--- a/segment_model.py
+++ b/segment_model.py
@@ -36,21 +36,16 @@ def ctc_lambda_func(args):
     return K.ctc_batch_cost(labels, y_pred, input_length, label_length)
 
 def segment_model(input_dim):
-    # input_dim = (100,100,1)
     inp = Input(shape=input_dim)
     cnv1 = Conv2D(filters=32, kernel_size=(5,9))(inp)
     cnv2 = Conv2D(filters=1, kernel_size=(5,9))(cnv1)
     dr_cnv2 = Dropout(rate=0.95)(cnv2)
-    # dr_cnv2
     cn_rnn_dim = (dr_cnv2.shape[1].value,dr_cnv2.shape[2].value)
     r_dr_cnv2 = Reshape(target_shape=cn_rnn_dim)(dr_cnv2)
     b_gr1 = Bidirectional(GRU(512, return_sequences=True),merge_mode='sum')(r_dr_cnv2)
-    # b_gr1
     b_gr2 = Bidirectional(GRU(512, return_sequences=True),merge_mode='sum')(b_gr1)
     b_gr3 = Bidirectional(GRU(512, return_sequences=True),merge_mode='sum')(b_gr2)
-    # b_gr3
     oup = Dense(2, activation='softmax')(b_gr3)
-    # oup
     return Model(inp, oup)
 
 def simple_segment_model(input_dim):